




import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
						from datasets import load_dataset



class    lowercase_		(__A ):
						lowerCAmelCase__			      ='''microsoft/speecht5_tts'''
						lowerCAmelCase__			      =(
						    '''This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '''
						    '''text to read (in English) and returns a waveform object containing the sound.'''
						)
						lowerCAmelCase__			      ='''text_reader'''
						lowerCAmelCase__			      =SpeechTaProcessor
						lowerCAmelCase__			      =SpeechTaForTextToSpeech
						lowerCAmelCase__			      =SpeechTaHifiGan

						lowerCAmelCase__			      =['''text''']
						lowerCAmelCase__			      =['''audio''']



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								if self.post_processor is None:
										SCREAMING_SNAKE_CASE_					      =							'microsoft/speecht5_hifigan'
								super().setup()



						def 	__a					(			self	:   int					,							snake_case__	:   Dict					,							snake_case__	:   Tuple=None  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.pre_processor(text=UpperCamelCase__					,							return_tensors='pt'					,							truncation=UpperCamelCase__  )

								if speaker_embeddings is None:
										if not is_datasets_available():
												raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'  )

										SCREAMING_SNAKE_CASE_					      =							load_dataset('Matthijs/cmu-arctic-xvectors'					,							split='validation'  )
										SCREAMING_SNAKE_CASE_					      =							torch.tensor(embeddings_dataset[73_05]['xvector']  ).unsqueeze(0  )

								return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



						def 	__a					(			self	:   str					,							snake_case__	:   Dict  ):

								"""simple docstring"""

								with torch.no_grad():
										return self.model.generate_speech(**UpperCamelCase__  )






						def 	__a					(			self	:   Dict					,							snake_case__	:   List[str]  ):

								"""simple docstring"""

								with torch.no_grad():
										return self.post_processor(UpperCamelCase__  ).cpu().detach()







'''simple docstring'''







def       __UpperCamelCase					(      lowercase_       :  list[int]   ,       lowercase_       :  list[int]   ,       lowercase_       :  int   ):
       """simple docstring"""




       return not any(
           neighbour == 1 and colored_vertices[i] == color
           for i, neighbour in enumerate(lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  list[list[int]]   ,       lowercase_       :  int   ,       lowercase_       :  list[int]   ,       lowercase_       :  int   ):
       """simple docstring"""




       if index == len(lowercase_   ):
              return True

       # Recursive Step
       for i in range(lowercase_   ):
              if valid_coloring(graph[index]   ,       lowercase_   ,       lowercase_   ):
                     # Color current vertex
                     a_						     =			i
                     # Validate coloring
                     if util_color(lowercase_   ,       lowercase_   ,       lowercase_   ,       index + 1   ):
                            return True
                     # Backtrack
                     a_						     =			-1
       return False


def       __UpperCamelCase					(      lowercase_       :  list[list[int]]   ,       lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[-1] * len(lowercase_   )

       if util_color(lowercase_   ,       lowercase_   ,       lowercase_   ,       0   ):
              return colored_vertices

       return []




